In wired networks such as the Internet, web advertising has provided a relatively large source of revenue for web content and other service providers. Web advertising includes targeted advertisements that are presented to specific users based on information associated with the users indicating that users may be interested in the targeted advertisements. A conventional approach to mining information associated with users for the purpose of generated targeted advertisements involves inserting detection triggers within commonly used services, such as web search, online purchase, or electronic mail, and storing per-user event records containing information based on the detected triggers.
The event records can include a wide variety of collected information, including search topics, keywords, visited uniform resource locators (URLs), electronic mail subjects, services used, time of usage, and so forth. Data mining techniques are then applied to the collected information to extract information from the event records to determine target advertisements that may be of interest to corresponding users. Although generally effective in producing targeted advertisements, conventional data mining techniques involve storage of a relatively large amount of data, which requires provision of a large and costly data storage and management infrastructure.
Although targeted advertisements can provide a relatively large source of revenue to service providers, the costly infrastructure that may have to be implemented for data mining purposes can dissuade some service providers, including service providers of wireless communications networks, from implementing this revenue opportunity.